library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.4 ✓ purrr 0.3.4
✓ tibble 3.1.2 ✓ dplyr 1.0.7
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.1
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
library(MatrixGenerics)
Loading required package: matrixStats
Attaching package: ‘matrixStats’
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count
Attaching package: ‘MatrixGenerics’
The following objects are masked from ‘package:matrixStats’:
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs,
colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys,
rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads,
rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs,
rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars
library(SingleCellExperiment)
Loading required package: SummarizedExperiment
Loading required package: GenomicRanges
Loading required package: stats4
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Loading required package: parallel
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anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,
is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which.max, which.min
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Attaching package: ‘S4Vectors’
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Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: ‘Biobase’
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anyMissing, rowMedians
lseq <- function(from, to, length.out){
exp(seq(log(from), log(to), length.out = length.out))
}
theme_set(cowplot::theme_cowplot())
library(org.Hs.eg.db)
Loading required package: AnnotationDbi
Attaching package: ‘AnnotationDbi’
The following object is masked from ‘package:dplyr’:
select
human_cell_cycle_genes <- select(org.Hs.eg.db, keytype = "GOALL", keys = "GO:0007049", columns = "ENSEMBL")[, "ENSEMBL"]
Registered S3 methods overwritten by 'htmltools':
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'select()' returned 1:many mapping between keys and columns
library(org.Mm.eg.db)
mouse_cell_cycle_genes <- select(org.Mm.eg.db, keytype = "GOALL", keys = "GO:0007049", columns = "ENSEMBL")[, "ENSEMBL"]
'select()' returned 1:many mapping between keys and columns
mu_sup <- lseq(1e-4, 1e6, length.out = 1001)
poisson_pred <- cross_df(list(mu = mu_sup, factor = 10^seq(-8, 8))) %>%
mutate(var = mu * factor)
gampoi_pred <- cross_df(list(mu = mu_sup, factor = 10^seq(-2, 2, by = 2))) %>%
mutate(var = mu + mu^2 * factor)
Prepare Data
Download data
# Work around for some bug in zellkonverter
# https://github.com/theislab/zellkonverter/issues/45
setAs("dgRMatrix", to = "dgCMatrix", function(from){
as(as(from, "CsparseMatrix"), "dgCMatrix")
})
if(! file.exists("../data/klein_2015.h5ad")){
download.file("https://data.caltech.edu/tindfiles/serve/f0d567c5-cea6-4a60-923e-e9fb4a4019e8/", "../data/klein_2015.h5ad")
}
if(! file.exists("../data/svensson_2017_1.h5ad")){
download.file("https://data.caltech.edu/tindfiles/serve/3f89d3a5-6ceb-486e-95d4-9bd3f511a706/", "../data/svensson_2017_1.h5ad")
}
if(! file.exists("../data/svensson_2017_2.h5ad")){
download.file("https://data.caltech.edu/tindfiles/serve/16dab9ea-4447-4e23-9aad-e68d052fd789/", "../data/svensson_2017_2.h5ad")
}
if(! file.exists("../data/nih3t3.h5ad")){
download.file("https://data.caltech.edu/tindfiles/serve/a448e98e-89cd-47b3-a134-803bbde29781/", "../data/nih3t3.h5ad")
}
if(! file.exists("../data/hek293t.h5ad")){
download.file("https://data.caltech.edu/tindfiles/serve/b2758046-9247-43ab-b8f0-68882b4f39a3/", "../data/hek293t.h5ad")
}
if(! file.exists("../data/nci-h1975.Rds")){
download.file("https://github.com/LuyiTian/sc_mixology/raw/master/data/csv/sc_10x.metadata.csv.gz", "../data/nci-h1975-metadata.csv.gz")
meta <- read.csv("../data/nci-h1975-metadata.csv.gz")
meta$cell_id <- rownames(meta)
download.file("https://github.com/LuyiTian/sc_mixology/raw/master/data/csv/sc_10x.count.csv.gz", "../data/nci-h1975.csv.gz")
count_mat <- as.matrix(read.csv("../data/nci-h1975.csv.gz"))
gene_info <- AnnotationHub::AnnotationHub()[["AH53537"]] %>%
as.data.frame() %>%
group_by(gene_id) %>%
summarize(chromosome = dplyr::first(seqnames),
gene_name = dplyr::first(gene_name),
gene_biotype = dplyr::first(gene_biotype))
row_df <- tibble(gene_id = rownames(count_mat)) %>%
left_join((gene_info), by = "gene_id") %>%
as.data.frame()
saveRDS(SummarizedExperiment(S4Vectors::SimpleList(counts = count_mat), colData = meta, rowData = row_df), "../data/nci-h1975.Rds")
}
if(! file.exists( "../data/GSE126321.Rds")){
download.file("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE126321&format=file", "../data/GSE126321_RAW.tar")
dir.create("../data/GSE126321")
untar("../data/GSE126321_RAW.tar", exdir = "../data/GSE126321")
mat <- Matrix::readMM("../data/GSE126321/GSM3596320_GM18502_matrix.mtx.gz")
genes <- read_tsv("../data/GSE126321/GSM3596320_GM18502_genes.tsv.gz", col_names = c("gene_id", "gene_name"))
barcodes <- read_tsv("../data/GSE126321/GSM3596320_GM18502_barcodes.tsv.gz", col_names = "barcode") %>%
mutate(barcode = str_remove(barcode, "-1"))
qc <- read.delim("../data/GSE126321/GSM3596320_GM18502_cellQC.tsv.gz", sep = "\t") %>%
rownames_to_column("barcode") %>%
as_tibble()
col_df <- left_join(barcodes, qc, by = "barcode") %>%
as.data.frame()
gene_info <- AnnotationHub::AnnotationHub()[["AH53537"]] %>%
as.data.frame() %>%
group_by(gene_id) %>%
summarize(chromosome = dplyr::first(seqnames),
gene_name = dplyr::first(gene_name),
gene_biotype = dplyr::first(gene_biotype))
row_df <- genes %>%
left_join((gene_info), by = "gene_id") %>%
transmute(gene_id, gene_name = gene_name.x, chromosome, gene_biotype) %>%
as.data.frame()
count_mat <- as(mat, "dgCMatrix")
rownames(count_mat) <- row_df$gene_id
colnames(count_mat) <- col_df$barcode
saveRDS(SummarizedExperiment(S4Vectors::SimpleList(counts = count_mat), colData = col_df, rowData = row_df), "../data/GSE126321.Rds")
}
Technical control experiments
se <- zellkonverter::readH5AD("../data/klein_2015.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)
[1] 25213 424
mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
tibble(mu, var, ercc_gene = str_starts(rownames(se_red), "ERCC-")) %>%
mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
ggplot(aes(x = mu, y = var)) +
geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
geom_line(data = gampoi_pred %>% filter((factor == 100 & var < 1.5e3) | (factor != 100 & var < 4e3)),
aes(group = factor), color = "#DEB554", size = 0.8) +
geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
coord_fixed(expand = FALSE, clip = "off") +
ggrastr::geom_point_rast(aes(color = ercc_gene), size = 0.1, show.legend = FALSE) +
annotate(shadowtext:::GeomShadowText, x = 5e3, y = 5e3, label = expression(Var==mu),
hjust = 0, vjust = 0.5, angle = 45, size = 4, color = "black", bg.colour = "white") +
annotate(shadowtext:::GeomShadowText, x = sqrt(4e3), y = 4e3, label = expression(Var==mu+1*mu^2),
hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
annotate(shadowtext:::GeomShadowText, x = sqrt(5e3 / 0.01), y = 5e3, label = expression(Var==mu+0.01*mu^2),
hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
annotate(shadowtext:::GeomShadowText, x = sqrt(2e3 / 100), y = 2e3, label = expression(Var==mu+100*mu^2),
hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Mean~(mu))) +
scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Variance)) +
scale_color_manual(values = c("TRUE" = "#6c58d1", "FALSE" = "black")) +
ggtitle("Klein 2015", subtitle = "<span style = 'color: #6c58d1'>ERCC spike-ins</span>") +
theme(plot.subtitle = ggtext::element_markdown())
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 300 row(s) containing missing values (geom_path).
Warning: Removed 100 row(s) containing missing values (geom_path).
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'

tech_p1 <- last_plot()
se <- zellkonverter::readH5AD("../data/svensson_2017_1.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)
[1] 17906 894
mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
tibble(mu, var, ercc_gene = str_starts(rownames(se_red), "ERCC-")) %>%
mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
ggplot(aes(x = mu, y = var)) +
geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
# geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
# geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
coord_fixed(expand = FALSE, clip = "off") +
ggrastr::geom_point_rast(aes(color = ercc_gene), size = 0.1, show.legend = FALSE) +
annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Mean~(mu))) +
scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Variance)) +
scale_color_manual(values = c("TRUE" = "#6c58d1", "FALSE" = "black")) +
ggtitle("Svensson 2017 (1)", subtitle = "") +
theme(plot.subtitle = ggtext::element_markdown())
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).

tech_p2 <- last_plot()
se <- zellkonverter::readH5AD("../data/svensson_2017_2.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)
[1] 18812 803
mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
tibble(mu, var, ercc_gene = str_starts(rownames(se_red), "ERCC-")) %>%
mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
ggplot(aes(x = mu, y = var)) +
geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
# geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
# geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
coord_fixed(expand = FALSE, clip = "off") +
ggrastr::geom_point_rast(aes(color = ercc_gene), size = 0.1, show.legend = FALSE) +
annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Mean~(mu))) +
scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Variance)) +
scale_color_manual(values = c("TRUE" = "#6c58d1", "FALSE" = "black")) +
ggtitle("Svensson 2017 (2)", subtitle = "") +
theme(plot.subtitle = ggtext::element_markdown())
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).

tech_p3 <- last_plot()
Biological control
se <- zellkonverter::readH5AD("../data/nih3t3.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)
[1] 19406 788
mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% paste0("mm10_", mouse_cell_cycle_genes)) %>%
mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
ggplot(aes(x = mu, y = var)) +
geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
geom_line(data = gampoi_pred %>% filter((factor == 100 & var < 1.5e3) | (factor != 100 & var < 4e3)),
aes(group = factor), color = "#DEB554", size = 0.8) +
geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
# geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
# geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
coord_fixed(expand = FALSE, clip = "off") +
# geom_point(size = 0.3, alpha = 0.3) +
ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
annotate(shadowtext:::GeomShadowText, x = 5e3, y = 5e3, label = expression(Var==mu),
hjust = 0, vjust = 0.5, angle = 45, size = 4, color = "black", bg.colour = "white") +
annotate(shadowtext:::GeomShadowText, x = sqrt(4e3), y = 4e3, label = expression(Var==mu+1*mu^2),
hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
annotate(shadowtext:::GeomShadowText, x = sqrt(5e3 / 0.01), y = 5e3, label = expression(Var==mu+0.01*mu^2),
hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
annotate(shadowtext:::GeomShadowText, x = sqrt(2e3 / 100), y = 2e3, label = expression(Var==mu+100*mu^2),
hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Mean~(mu))) +
scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Variance)) +
scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
ggtitle("NIH/3T3 Cells", subtitle = "<span style = 'color: #87172f'>Cell cycle marker</span> genes.") +
theme(plot.subtitle = ggtext::element_markdown())
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 300 row(s) containing missing values (geom_path).
Warning: Removed 100 row(s) containing missing values (geom_path).
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'

p1 <- last_plot()
se <- zellkonverter::readH5AD("../data/hek293t.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)
[1] 22804 565
mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% paste0("hg19_", human_cell_cycle_genes)) %>%
mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
ggplot(aes(x = mu, y = var)) +
geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
# geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
# geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
coord_fixed(expand = FALSE, clip = "off") +
ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Mean~(mu))) +
scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Variance)) +
scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
ggtitle("HEK 293T Cells", subtitle = "") +
theme(plot.subtitle = ggtext::element_markdown())
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).

p2 <- last_plot()
se_full <- readRDS("../data/nci-h1975.Rds")
table(se_full$cell_line)
H1975 H2228 HCC827
313 315 274
se <- se_full[, se_full$cell_line == "H1975"]
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)
[1] 15932 99
mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% human_cell_cycle_genes) %>%
mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
ggplot(aes(x = mu, y = var)) +
geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
# geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
# geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
coord_fixed(expand = FALSE, clip = "off") +
ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Mean~(mu))) +
scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Variance)) +
scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
ggtitle("NCI-H1975 Cells", subtitle = "") +
theme(plot.subtitle = ggtext::element_markdown())
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).

p3 <- last_plot()
se <- readRDS("../data/GSE126321.Rds")
# table(se$cellPhase)
# se <- se[, se$cellPhase == "S"]
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)
[1] 17909 1242
mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% human_cell_cycle_genes) %>%
mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
ggplot(aes(x = mu, y = var)) +
geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
# geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
# geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
coord_fixed(expand = FALSE, clip = "off") +
ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Mean~(mu))) +
scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
labels = c(expression(10^-3), expression(10^-1),
expression(10), expression(10^3), expression(10^5)),
name = expression(Variance)) +
scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
ggtitle("GM18502 Cells", subtitle = "") +
theme(plot.subtitle = ggtext::element_markdown())
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).
Warning: Removed 1812 rows containing missing values (geom_point).

p4 <- last_plot()
p_tech <- cowplot::plot_grid(tech_p1, tech_p2, tech_p3, NULL, nrow = 1, align = "h")
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 300 row(s) containing missing values (geom_path).
Warning: Removed 100 row(s) containing missing values (geom_path).
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).
p_bio <- cowplot::plot_grid(p1, p2, p3, p4, nrow = 1, align = "h")
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 300 row(s) containing missing values (geom_path).
Warning: Removed 100 row(s) containing missing values (geom_path).
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning in is.na(x) :
is.na() applied to non-(list or vector) of type 'expression'
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).
Warning: Removed 9803 row(s) containing missing values (geom_path).
Warning: Removed 1203 row(s) containing missing values (geom_path).
Warning: Removed 200 row(s) containing missing values (geom_path).
Warning: Removed 1812 rows containing missing values (geom_point).
tech_title <- cowplot::ggdraw() + cowplot::draw_label("(A) Droplets with RNA solution (technical control)",
fontface = "bold", x = 0.02, hjust = 0, size = 18)
bio_title <- cowplot::ggdraw() + cowplot::draw_label("(B) Cell line populations (biological control)",
fontface = "bold", x = 0.02, hjust = 0, size = 18)
p_res <- cowplot::plot_grid(tech_title, p_tech, bio_title, p_bio, ncol = 1,
rel_heights = c(0.2, 1, 0.2, 1))
p_res

cowplot::save_plot("../output/mean_variance_relation_homnogeneous_cells.pdf", p_res, nrow = 2, ncol = 4, base_asp = 0.68, base_height = 5)
Session Info
sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] org.Mm.eg.db_3.13.0 org.Hs.eg.db_3.13.0 AnnotationDbi_1.54.1 SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0 Biobase_2.52.0
[7] GenomicRanges_1.44.0 GenomeInfoDb_1.28.0 IRanges_2.26.0 S4Vectors_0.30.0 BiocGenerics_0.38.0 MatrixGenerics_1.4.0
[13] matrixStats_0.59.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_1.4.0
[19] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.4 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_2.0-1 ellipsis_0.3.2 markdown_1.1 XVector_0.32.0 fs_1.5.0
[7] gridtext_0.1.4 ggtext_0.1.1 rstudioapi_0.13 farver_2.1.0 bit64_4.0.5 interactiveDisplayBase_1.30.0
[13] fansi_0.5.0 lubridate_1.7.10 xml2_1.3.2 sparseMatrixStats_1.4.0 cachem_1.0.5 knitr_1.33
[19] jsonlite_1.7.2 Cairo_1.5-12.2 broom_0.7.7 dbplyr_2.1.1 png_0.1-7 shiny_1.6.0
[25] BiocManager_1.30.16 compiler_4.1.0 httr_1.4.2 basilisk_1.4.0 backports_1.2.1 assertthat_0.2.1
[31] Matrix_1.3-4 fastmap_1.1.0 cli_2.5.0 later_1.2.0 htmltools_0.5.1.1 tools_4.1.0
[37] gtable_0.3.0 glue_1.4.2 GenomeInfoDbData_1.2.6 rappdirs_0.3.3 Rcpp_1.0.6 cellranger_1.1.0
[43] vctrs_0.3.8 Biostrings_2.60.1 zellkonverter_1.2.0 xfun_0.24 rvest_1.0.0 mime_0.10
[49] lifecycle_1.0.0 AnnotationHub_3.0.0 zlibbioc_1.38.0 scales_1.1.1 basilisk.utils_1.4.0 ragg_1.1.3
[55] hms_1.1.0 promises_1.2.0.1 yaml_2.2.1 curl_4.3.1 memoise_2.0.0 reticulate_1.20
[61] ggrastr_0.2.3 stringi_1.6.2 RSQLite_2.2.7 BiocVersion_3.13.1 filelock_1.0.2 systemfonts_1.0.2
[67] rlang_0.4.11 pkgconfig_2.0.3 bitops_1.0-7 lattice_0.20-44 shadowtext_0.0.8 cowplot_1.1.1
[73] bit_4.0.4 tidyselect_1.1.1 magrittr_2.0.1 R6_2.5.0 generics_0.1.0 DelayedArray_0.18.0
[79] DBI_1.1.1 pillar_1.6.1 haven_2.4.1 withr_2.4.2 KEGGREST_1.32.0 RCurl_1.98-1.3
[85] dir.expiry_1.0.0 modelr_0.1.8 crayon_1.4.1 utf8_1.2.1 BiocFileCache_2.0.0 grid_4.1.0
[91] readxl_1.3.1 blob_1.2.1 reprex_2.0.0 digest_0.6.27 xtable_1.8-4 httpuv_1.6.1
[97] textshaping_0.3.5 munsell_0.5.0 beeswarm_0.4.0 vipor_0.4.5
---
title: "R Notebook"
output: html_notebook
---


```{r}
library(tidyverse)
library(MatrixGenerics)
library(SingleCellExperiment)
```

```{r}
lseq <- function(from, to, length.out){
  exp(seq(log(from), log(to), length.out = length.out))
}
theme_set(cowplot::theme_cowplot())
```



```{r}
library(org.Hs.eg.db)
human_cell_cycle_genes <- select(org.Hs.eg.db, keytype = "GOALL", keys = "GO:0007049", columns = "ENSEMBL")[, "ENSEMBL"]
library(org.Mm.eg.db)
mouse_cell_cycle_genes <- select(org.Mm.eg.db, keytype = "GOALL", keys = "GO:0007049", columns = "ENSEMBL")[, "ENSEMBL"]
```


```{r}
mu_sup <- lseq(1e-4, 1e6, length.out = 1001)
poisson_pred <- cross_df(list(mu = mu_sup, factor = 10^seq(-8, 8))) %>%
  mutate(var = mu * factor)

gampoi_pred <- cross_df(list(mu = mu_sup, factor = 10^seq(-2, 2, by = 2))) %>%
  mutate(var = mu + mu^2 * factor) 
```

# Prepare Data


## Download data

```{r}
# Work around for some bug in zellkonverter
# https://github.com/theislab/zellkonverter/issues/45
setAs("dgRMatrix", to = "dgCMatrix", function(from){
  as(as(from, "CsparseMatrix"), "dgCMatrix")
})
```


```{r}
if(! file.exists("../data/klein_2015.h5ad")){
  download.file("https://data.caltech.edu/tindfiles/serve/f0d567c5-cea6-4a60-923e-e9fb4a4019e8/", "../data/klein_2015.h5ad")
}
if(! file.exists("../data/svensson_2017_1.h5ad")){
  download.file("https://data.caltech.edu/tindfiles/serve/3f89d3a5-6ceb-486e-95d4-9bd3f511a706/", "../data/svensson_2017_1.h5ad")
}
if(! file.exists("../data/svensson_2017_2.h5ad")){
  download.file("https://data.caltech.edu/tindfiles/serve/16dab9ea-4447-4e23-9aad-e68d052fd789/", "../data/svensson_2017_2.h5ad")
}
if(! file.exists("../data/nih3t3.h5ad")){
  download.file("https://data.caltech.edu/tindfiles/serve/a448e98e-89cd-47b3-a134-803bbde29781/", "../data/nih3t3.h5ad")
}
if(! file.exists("../data/hek293t.h5ad")){
  download.file("https://data.caltech.edu/tindfiles/serve/b2758046-9247-43ab-b8f0-68882b4f39a3/", "../data/hek293t.h5ad")
}
if(! file.exists("../data/nci-h1975.Rds")){
  download.file("https://github.com/LuyiTian/sc_mixology/raw/master/data/csv/sc_10x.metadata.csv.gz", "../data/nci-h1975-metadata.csv.gz") 
  meta <- read.csv("../data/nci-h1975-metadata.csv.gz")
  meta$cell_id <- rownames(meta)
  
  download.file("https://github.com/LuyiTian/sc_mixology/raw/master/data/csv/sc_10x.count.csv.gz", "../data/nci-h1975.csv.gz") 
  count_mat <- as.matrix(read.csv("../data/nci-h1975.csv.gz"))
  gene_info <- AnnotationHub::AnnotationHub()[["AH53537"]] %>%
    as.data.frame() %>%
    group_by(gene_id) %>%
    summarize(chromosome = dplyr::first(seqnames),
              gene_name = dplyr::first(gene_name),
              gene_biotype = dplyr::first(gene_biotype))
  row_df <- tibble(gene_id = rownames(count_mat)) %>%
    left_join((gene_info), by = "gene_id") %>%
    as.data.frame()
  saveRDS(SummarizedExperiment(S4Vectors::SimpleList(counts = count_mat), colData = meta, rowData = row_df), "../data/nci-h1975.Rds")
}
if(! file.exists( "../data/GSE126321.Rds")){
  download.file("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE126321&format=file", "../data/GSE126321_RAW.tar")
  dir.create("../data/GSE126321")
  untar("../data/GSE126321_RAW.tar", exdir = "../data/GSE126321")
  mat <- Matrix::readMM("../data/GSE126321/GSM3596320_GM18502_matrix.mtx.gz")
  genes <- read_tsv("../data/GSE126321/GSM3596320_GM18502_genes.tsv.gz", col_names = c("gene_id", "gene_name"))
  barcodes <- read_tsv("../data/GSE126321/GSM3596320_GM18502_barcodes.tsv.gz", col_names = "barcode") %>%
    mutate(barcode = str_remove(barcode, "-1"))
  qc <- read.delim("../data/GSE126321/GSM3596320_GM18502_cellQC.tsv.gz", sep = "\t") %>%
    rownames_to_column("barcode")  %>%
    as_tibble()
  col_df <- left_join(barcodes, qc, by = "barcode") %>%
    as.data.frame()
  gene_info <- AnnotationHub::AnnotationHub()[["AH53537"]] %>%
    as.data.frame() %>%
    group_by(gene_id) %>%
    summarize(chromosome = dplyr::first(seqnames),
              gene_name = dplyr::first(gene_name),
              gene_biotype = dplyr::first(gene_biotype))
  row_df <- genes %>%
    left_join((gene_info), by = "gene_id") %>%
    transmute(gene_id, gene_name = gene_name.x, chromosome, gene_biotype) %>%
    as.data.frame()
  count_mat <- as(mat, "dgCMatrix")
  rownames(count_mat) <- row_df$gene_id
  colnames(count_mat) <- col_df$barcode
  saveRDS(SummarizedExperiment(S4Vectors::SimpleList(counts = count_mat), colData = col_df, rowData = row_df), "../data/GSE126321.Rds")
}

```


# Technical control experiments

```{r}
se <- zellkonverter::readH5AD("../data/klein_2015.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)

mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
```


```{r}
tibble(mu, var, ercc_gene = str_starts(rownames(se_red), "ERCC-")) %>%
  mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
  ggplot(aes(x = mu, y = var)) +
    geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
    geom_line(data = gampoi_pred %>% filter((factor == 100 & var < 1.5e3) | (factor != 100 & var < 4e3)), 
              aes(group = factor), color = "#DEB554", size = 0.8) +
    geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
    coord_fixed(expand = FALSE, clip = "off") +
    ggrastr::geom_point_rast(aes(color = ercc_gene), size = 0.1, show.legend = FALSE) +
    annotate(shadowtext:::GeomShadowText, x = 5e3, y = 5e3, label = expression(Var==mu),
             hjust = 0, vjust = 0.5, angle = 45, size = 4, color = "black", bg.colour = "white") +
    annotate(shadowtext:::GeomShadowText, x = sqrt(4e3), y = 4e3, label = expression(Var==mu+1*mu^2),
             hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
    annotate(shadowtext:::GeomShadowText, x = sqrt(5e3 / 0.01), y = 5e3, label = expression(Var==mu+0.01*mu^2),
             hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
    annotate(shadowtext:::GeomShadowText, x = sqrt(2e3 / 100), y = 2e3, label = expression(Var==mu+100*mu^2),
             hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
    annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
                        short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
    scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Mean~(mu))) +
    scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Variance)) +
    scale_color_manual(values = c("TRUE" = "#6c58d1", "FALSE" = "black")) +
    ggtitle("Klein 2015", subtitle = "<span style = 'color: #6c58d1'>ERCC spike-ins</span>") +
    theme(plot.subtitle = ggtext::element_markdown())


tech_p1 <- last_plot()
```


```{r}
se <- zellkonverter::readH5AD("../data/svensson_2017_1.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)

mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
```


```{r}
tibble(mu, var, ercc_gene = str_starts(rownames(se_red), "ERCC-")) %>%
  mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
  ggplot(aes(x = mu, y = var)) +
    geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
    # geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
    # geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
    geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
    geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
    coord_fixed(expand = FALSE, clip = "off") +
    ggrastr::geom_point_rast(aes(color = ercc_gene), size = 0.1, show.legend = FALSE) +
    annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
                        short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
    scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Mean~(mu))) +
    scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Variance)) +
    scale_color_manual(values = c("TRUE" = "#6c58d1", "FALSE" = "black")) +
    ggtitle("Svensson 2017 (1)", subtitle = "") +
    theme(plot.subtitle = ggtext::element_markdown())

tech_p2 <- last_plot()
```


```{r}
se <- zellkonverter::readH5AD("../data/svensson_2017_2.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)

mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
```


```{r}
tibble(mu, var, ercc_gene = str_starts(rownames(se_red), "ERCC-")) %>%
  mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
  ggplot(aes(x = mu, y = var)) +
    geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
    # geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
    # geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
    geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
    geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
    coord_fixed(expand = FALSE, clip = "off") +
    ggrastr::geom_point_rast(aes(color = ercc_gene), size = 0.1, show.legend = FALSE) +
    annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
                        short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
    scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Mean~(mu))) +
    scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Variance)) +
    scale_color_manual(values = c("TRUE" = "#6c58d1", "FALSE" = "black")) +
    ggtitle("Svensson 2017 (2)", subtitle = "") +
    theme(plot.subtitle = ggtext::element_markdown())

tech_p3 <- last_plot()
```



# Biological control


```{r}
se <- zellkonverter::readH5AD("../data/nih3t3.h5ad")
sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)

mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
```


```{r}
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% paste0("mm10_", mouse_cell_cycle_genes)) %>%
  mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
  ggplot(aes(x = mu, y = var)) +
    geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
    geom_line(data = gampoi_pred %>% filter((factor == 100 & var < 1.5e3) | (factor != 100 & var < 4e3)), 
              aes(group = factor), color = "#DEB554", size = 0.8) +
    geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
    # geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
    # geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
    coord_fixed(expand = FALSE, clip = "off") +
    # geom_point(size = 0.3, alpha = 0.3) +
    ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
    annotate(shadowtext:::GeomShadowText, x = 5e3, y = 5e3, label = expression(Var==mu),
             hjust = 0, vjust = 0.5, angle = 45, size = 4, color = "black", bg.colour = "white") +
    annotate(shadowtext:::GeomShadowText, x = sqrt(4e3), y = 4e3, label = expression(Var==mu+1*mu^2),
             hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
    annotate(shadowtext:::GeomShadowText, x = sqrt(5e3 / 0.01), y = 5e3, label = expression(Var==mu+0.01*mu^2),
             hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
    annotate(shadowtext:::GeomShadowText, x = sqrt(2e3 / 100), y = 2e3, label = expression(Var==mu+100*mu^2),
             hjust = 0, vjust = 0.4, angle = atan(2) / pi * 180, size = 4, color = "black", bg.colour = "white") +
    annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
                        short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
    scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Mean~(mu))) +
    scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Variance)) +
    scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
    ggtitle("NIH/3T3 Cells", subtitle = "<span style = 'color: #87172f'>Cell cycle marker</span> genes.") +
    theme(plot.subtitle = ggtext::element_markdown())


p1 <- last_plot()
```




```{r}
se <- zellkonverter::readH5AD("../data/hek293t.h5ad")

sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)

mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))


```



```{r}
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% paste0("hg19_", human_cell_cycle_genes)) %>%
  mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
  ggplot(aes(x = mu, y = var)) +
    geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
    # geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
    # geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
    geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
    geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
    coord_fixed(expand = FALSE, clip = "off") +
    ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
    annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
                        short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
    scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Mean~(mu))) +
    scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Variance)) +
    scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
    ggtitle("HEK 293T Cells", subtitle = "") +
    theme(plot.subtitle = ggtext::element_markdown())

p2 <- last_plot()
```



```{r}
se_full <- readRDS("../data/nci-h1975.Rds")
table(se_full$cell_line)
```

```{r}
se <- se_full[, se_full$cell_line == "H1975"]

sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)

mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
```


```{r}
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% human_cell_cycle_genes) %>%
  mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
  ggplot(aes(x = mu, y = var)) +
    geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
    # geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
    # geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
    geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
    geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
    coord_fixed(expand = FALSE, clip = "off") +
    ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
    annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
                        short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
    scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Mean~(mu))) +
    scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Variance)) +
    scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
    ggtitle("NCI-H1975 Cells", subtitle = "") +
    theme(plot.subtitle = ggtext::element_markdown())

p3 <- last_plot()
```



```{r}
se <- readRDS("../data/GSE126321.Rds")
# table(se$cellPhase)
# se <- se[, se$cellPhase == "S"]

sf <- colSums2(assay(se))
thres <- quantile(sf, 0.5) * c(1, 1.3)
hist(sf, breaks = 50); abline(v = thres, col = "red", lwd = 2)

se_red <- se[, sf > thres[1] & sf < thres[2]]
sf_red <- sf[sf > thres[1] & sf < thres[2]]
se_red <- se_red[rowSums2(assay(se_red)) > 0]
dim(se_red)

mu <- rowMeans2(assay(se_red))
var <- rowVars(assay(se_red))
```


```{r}
tibble(mu, var, cell_cycle_gene = rownames(se_red) %in% human_cell_cycle_genes) %>%
  mutate(mu_pred = mu, mu_sq_pred = mu^2) %>%
  ggplot(aes(x = mu, y = var)) +
    geom_line(data = poisson_pred, aes(group = factor), color = "lightgray", size = 0.1) +
    # geom_line(data = gampoi_pred %>% filter(var < 4e3), aes(group = factor), color = "#DEB554", size = 0.8) +
    # geom_line(data = poisson_pred %>% filter(factor == 1 & mu < 4e3), aes(group = factor), color = "#C981DE", size = 1.2) +
    geom_line(data = gampoi_pred, aes(group = factor), color = "#DEB554", size = 0.8) +
    geom_line(data = poisson_pred %>% filter(factor == 1), aes(group = factor), color = "#C981DE", size = 1.2) +
    coord_fixed(expand = FALSE, clip = "off") +
    ggrastr::geom_point_rast(aes(color = cell_cycle_gene), size = 0.1, show.legend = FALSE) +
    annotation_logticks(scaled = TRUE, outside = FALSE, size = 0.2,
                        short = unit(0.05, "cm"), mid = unit(0.1, "cm"), long = unit(0.15, "cm")) +
    scale_x_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e5),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Mean~(mu))) +
    scale_y_log10(breaks = c(0.001, 0.1, 10, 1000, 1e5), limits = c(1e-3, 1e6),
                  labels = c(expression(10^-3), expression(10^-1), 
                             expression(10),  expression(10^3), expression(10^5)),
                  name = expression(Variance)) +
    scale_color_manual(values = c("TRUE" = "#87172f", "FALSE" = "black")) +
    ggtitle("GM18502 Cells", subtitle = "") +
    theme(plot.subtitle = ggtext::element_markdown())

p4 <- last_plot()
```




```{r, fig.width = 12, fig.heigh = 8}
p_tech <- cowplot::plot_grid(tech_p1, tech_p2, tech_p3, NULL, nrow = 1, align = "h")
p_bio <- cowplot::plot_grid(p1, p2, p3, p4, nrow = 1, align = "h")

tech_title <- cowplot::ggdraw() + cowplot::draw_label("(A) Droplets with RNA solution (technical control)", 
                                                      fontface = "bold", x = 0.02, hjust = 0, size = 18)
bio_title <- cowplot::ggdraw() + cowplot::draw_label("(B) Cell line populations (biological control)", 
                                                     fontface = "bold", x = 0.02, hjust = 0, size = 18)

p_res <- cowplot::plot_grid(tech_title, p_tech, bio_title, p_bio, ncol = 1,
                            rel_heights = c(0.2, 1, 0.2, 1))
p_res
```


```{r}
cowplot::save_plot("../output/mean_variance_relation_homnogeneous_cells.pdf", p_res, nrow = 2, ncol = 4, base_asp = 0.68, base_height = 5)
```







# Session Info

```{r}
sessionInfo()
```

